Supersede: Diagnosing and Training the Memory-Update Gap in LLM Agents
Large language model (LLM) agents struggle to update facts in long-term interactions. Replacing full context with bounded memory drops accuracy from 92% to 77% even on frontier models. The gap scales with conversation length, not memory size. The authors introduce Supersede, a reinforcement learning environment that trains agents to prioritize current facts over superseded ones. Fine-tuning Qwen2.5-3B in this environment nearly doubles held-out accuracy (9.0% to 16.7%).
[2606.27472] Supersede: Diagnosing and Training the Memory-Update Gap in LLM Agents
[Submitted on 25 Jun 2026]
Title:Supersede: Diagnosing and Training the Memory-Update Gap in LLM Agents
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Abstract:Large language model (LLM) agents operate over long, multi-session interactions in which facts change: a user moves, a price updates, a plan is revised. Acting correctly requires using the current value of a fact and discarding values that have been superseded. We isolate this ability on real conversational data and show that it is a distinct, unsolved failure. On the knowledge-update subset of LongMemEval, replacing an agent's full context with a bounded, self-maintained memory drops accuracy from 92% to 77% even on a frontier model (gpt-5.4), a gap that is statistically significant (paired McNemar p
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